Without Writing Out the Standard, Your AI SDLC Will Struggle — Introducing the AI Software Engineer, a Silly Name for a Serious Problem
You rolled out the training, bought the licenses, ran the pilots, and your engineering org is producing almost exactly what it was two years ago. The problem was never the training. The problem is that you never set a new…
Organizational standards define the operational reality and are the executive's sole prerogative.
Establish the new operating standard, or incur the AI debt.
A standard is an observable demonstration of competency, measured against current work output, not a certification of training hours or course completion. It is a dynamic target, not a static credential.
Organizations that self-measure AI adoption without an explicit standard will maintain flat output while incurring increased tooling costs. This creates a governance vacuum, not a transformation.
New standards must apply across all functions, not just engineering. Failing to redefine roles like product management, design, and program management creates new bottlenecks that absorb productivity gains.
The most effective way to implement a new standard is to define the role, specify a qualification path, set a non-negotiable timeline, and provide real support. Hold the line on the standard itself.
Compensation bands must reflect the new standard, rewarding those who qualify with above-market rates. Delaying this adjustment leads to adverse selection, where high performers leave and those with limited options remain.
The first question for any AI program: who is defining the new standard, and what are the consequences of not meeting it?
Organizational standards define the operational reality and are the executive's sole prerogative.
Establish the new operating standard, or incur the AI debt.
A standard is an observable demonstration of competency, measured against current work output, not a certification of training hours or course completion. It is a dynamic target, not a static credential.
Organizations that self-measure AI adoption without an explicit standard will maintain flat output while incurring increased tooling costs. This creates a governance vacuum, not a transformation.
New standards must apply across all functions, not just engineering. Failing to redefine roles like product management, design, and program management creates new bottlenecks that absorb productivity gains.
The most effective way to implement a new standard is to define the role, specify a qualification path, set a non-negotiable timeline, and provide real support. Hold the line on the standard itself.
Compensation bands must reflect the new standard, rewarding those who qualify with above-market rates. Delaying this adjustment leads to adverse selection, where high performers leave and those with limited options remain.
The first question for any AI program: who is defining the new standard, and what are the consequences of not meeting it?
After 20 years in software development, Norman is both a hands-on leader and defining the new age of AI SDLC for some of the biggest brands in the world — and exploring it with the builders. He writes here about things he is hearing and seeing. All posts are his personal points of view and do not reflect any employer or any customer he has ever had contact with.
The views and opinions expressed in this article are the author’s own and do not represent the positions of any employer, client, or affiliated organization.
